{
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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Pipeline Anova SVM\n\n\nSimple usage of Pipeline that runs successively a univariate\nfeature selection with anova and then a C-SVM of the selected features.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
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      "source": [
        "from sklearn import svm\nfrom sklearn.datasets import samples_generator\nfrom sklearn.feature_selection import SelectKBest, f_regression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\n\nprint(__doc__)\n\n# import some data to play with\nX, y = samples_generator.make_classification(\n    n_features=20, n_informative=3, n_redundant=0, n_classes=4,\n    n_clusters_per_class=2)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n\n# ANOVA SVM-C\n# 1) anova filter, take 3 best ranked features\nanova_filter = SelectKBest(f_regression, k=3)\n# 2) svm\nclf = svm.SVC(kernel='linear')\n\nanova_svm = make_pipeline(anova_filter, clf)\nanova_svm.fit(X_train, y_train)\ny_pred = anova_svm.predict(X_test)\nprint(classification_report(y_test, y_pred))"
      ]
    }
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